Compression of ECG Signal Based on Improving the Signal Sparsity Adopting QRS-Complex Estimation and Spatial Domain Transforms | ||||
| Menoufia Journal of Electronic Engineering Research | ||||
| Article 4, Volume 25, Issue 2, July 2016, Page 179-190 | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/mjeer.2016.64095 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| Mohammed Abo-Zahhad1; Aziza Hussein2; Abdelfatah Mohamed1 | ||||
| 1Electrical and Electronics Eng. Dept., Faculty of Eng., Assiut University, Egypt. | ||||
| 2Computer and Systems Dept., Faculty of Engineering, Minia University, Egypt. | ||||
| Abstract | ||||
| In this paper, an ECG signal compression technique based on improving the signal sparsity adopting QRS-complex estimation and spatial domain transforms is proposed. Compressive Sensing (CS) framework is utilized for this purpose; where the difference between the rate of change of a signal and the rate of change of its information contents is utilized. The proposed method starts with estimating the QRS-complex through the detection of the maximum amplitudes and the start and end points of the signal components. The error signal calculated as the difference between the estimated QRS-complex samples and original ECG samples is transformed using either DWT, DCT or FFT. QRS-complex estimation results in sparser timedomain error signal and the sparsity is increased by adopting the spatial-domain transforms. The proposed technique is assessed by calculating PRD and CR. Numerical results indicate that DWT gives a higher CR and lower PRD. The effect of increasing the signal sparsity, signal length, wavelet filters and wavelet decomposition levels have been studied. Comparison with recently published results [1] adopted only QRS-estimation and DWT indicate that the utilization of CS with both QRS-estimation and DWT yields to improved acceptable retrieved signal quality and higher CR. Moreover, comparison with other four CS-based algorithms [2]-[5] and other traditional ECG compression algorithm [6] indicates the superior performance of the proposed algorithm. . | ||||
| References | ||||
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xt-stroke-width: 0px; "> [1] M. Abo-Zahhad, S. M. Ahamed and A. Zakaria, "ECG Signal Compression Technique Based on Discrete Wavelet Transform and QRS-Complex Estimation," Signal Processing – An International Journal (SPIJ), vol. 4, no. 2, pp. 138-160, 2010. [2] A.M.R. Dixon, E.G. Allstot, D. Gangopadhyay, and D.J. Allstot, “Compressed sensing system considerations for ECG and EMG wireless biosensors,” IEEE TBCAS, vol. 6, no. 2, pp. 156 –166, April 2012. [3] L.F. Polania, R.E. Carrillo, M. Blanco-Velasco, and K.E. Barner, “Compressive sensing exploiting wavelet domain dependencies for ECG compression,” in SPIE Defense, Security, and Sensing, pp. 83650E–83650E, April 2012. [4] L.F. Polania, R.E. Carrillo, M. Blanco-Velasco, and K.E. Barner, “On exploiting interbeat correlation in compressive sensing-based ECG compression,” in SPIE Defense, Security, and Sensing, pp. 83650D– 83650D, April 2012. [5] L.F. Polania, R.E. Carrillo, M. Blanco-Velasco, and K.E. Barner, “Compressed sensing based method for ECG compression,” in Proceedings e-width: 0px; "> IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, Prague, Czech Republic, ICASSP, , pp. 761-764, May 2011. [6] Z. Lu, D. Y. Kim, and W. A. Pearlman, “Wavelet compression of ECG signals by the set partitioning in hierarchical trees algorithm,” IEEE T-BME, vol. 47, no. 7, pp. 849 –856, July 2000. [7] [7] D. L. Donoho, “Compressed sensing,” IEEE Trans. Inform. Theory, vol. 52, pp. 1289-1306, 2006. [8] [8] E. Candes, J. Romberg, and T. Tao, “Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inform. Theory, vol. 52, no. 2, pp. 489–509, Feb. 2006. [9] [9] M. M. Abo-Zahhad, Aziza I. Hussein, A. M. Mohamed, "Compression of ECG Signal Based on Compressive Sensing and the Extraction of Significant Features," Int. J. Communications, Network and System Sciences, vol. 8, pp. 97-117, April 2015. [10] [10] A. B. Roy, D. Dey and B. Mohanty and D. Banerjee, "Comparison of FFT, DCT, DWT, WHT Compression Techniques on Electrocardiogram and Photoplethysmography Signals" Special Issue of International Journal of Computer Applications (Inte. Conf. on Computing, Communication and Sensor Network, CCSN'2012), pp. 6-11, 2012. | ||||
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